It has been recognized that point sets can serve as an effective representation to obtain additional information of objects.
Several recent approaches learn representations to generate point clouds of shapes [1]}, [2]}, [3]}.
More closely targeted to robotics, Xu et al. [4]} and Wang et al. [5]} propose fusion networks to extract pixel-wise dense feature embeddings for estimating 3D bounding boxes and 6D object pose respectively. These approaches focus on robotic vision, and unlike them we do not rely on the 6D object pose as a label for training.
